Degree of Accuracy in Credit Card Fraud Detection Using Local Outlier Factor and Isolation Forest Algorithm

In this era of digitalization where everyone prefers online-based transactional activities, this increases the demand for a credit card, the fraudulent cases are increasing day by day which causes tremendous loss to an individual. Our model comprises 2 major algorithms and uses anomaly detection as a method to classify fraudulent transactions. With the help of these two algorithms i. e., local outlier factor and Isolation Forest. We are implementing our Machine Learning (ML) Model Credit Card Fraud Detection (CCFD) to get the highest possible degree of accuracy of fraud, these two algorithms in layman rs terms isolate the transaction or it can be considered as an outlier i.e., deviation from a normal and common order which have a high rate of anomaly or fraud transaction.